Continuous lesion images drive more accurate predictions of outcomes after stroke than binary lesion images

Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by treating the post-stroke prognostic problem as a machine learning problem. Consistent with the observation that these impairments are caused by the brain damage that stroke survivors suffer, information concerning where and how much lesion damage they have suffered conveys useful prognostic information for these models. Much recent research has considered how best to encode this lesion information, to maximise its prognostic value. Here, we consider an encoding that, while not novel, has never before been formally examined in this context: continuous lesion images, which encode continuous evidence for the presence of a lesion, both within and beyond what might otherwise be considered the boundary of a binary lesion image. Current state of the art models employ information derived from binary lesion images. Here, we show that those models are significantly improved (i.e., with smaller Mean Squared Error between predicted and empirical outcome scores) when using continuous lesion images to predict a wide range of cognitive and language outcomes from a very large sample of stroke patients. We use further model comparisons to locate the predictive advantage to the provision of continuous lesion evidence beyond the boundary of binary lesion images. The continuous lesion images thus provide a straightforward way to incorporate details of both lesioned and non-lesioned tissue when predicting outcomes after stroke.